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首页> 外文期刊>Journal of Civil Engineering and Management >GROUTABILITY PREDICTION OF MICROFINE CEMENT BASED SOIL IMPROVEMENT USING EVOLUTIONARY LS-SVM INFERENCE MODEL
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GROUTABILITY PREDICTION OF MICROFINE CEMENT BASED SOIL IMPROVEMENT USING EVOLUTIONARY LS-SVM INFERENCE MODEL

机译：基于进化LS-SVM推论模型的微细水泥基土壤改良剂成粒性预测

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摘要

Permeation grouting is a widely used technique for soil improvement in construction engineering. Thus, predicting the results of the grouting activity is a particularly interesting topic that has drawn the attention of researchers both from the academic field and industry. Recent literature has indicated that artificial intelligence (AI) approaches for grouta-bility prediction are capable of delivering better performance than traditional formula-based ones. In this study, a novel AI method, evolutionary Least Squares Support Vector Machine Inference Model for groutability prediction (ELSIM-GP), is proposed to forecast the result of grouting activity that utilizes microfine cement grout. In the model, Least Squares Support Vector Machine (LS-SVM) is a supervised machine learning technique that is employed to learn the decision boundary for classifying high dimensional data. Differential Evolution (DE) is integrated into ELSIM-GP for automatically optimizing its tuning parameters. 240 historical cases of grouting process for sandy silt soil have been collected to train, validate, and test the inference model. Experimental results demonstrated that ELSIM-GP can overcome other benchmark approaches in terms of forecasting accuracy. Therefore, the proposed approach is a promising alternative for predicting groutability.
机译：渗透灌浆是建筑工程中广泛使用的土壤改良技术。因此，预测灌浆活动的结果是一个特别有趣的话题，引起了学术界和工业界研究人员的关注。最近的文献表明，用于增长能力预测的人工智能（AI）方法比传统的基于公式的方法能够提供更好的性能。在这项研究中，提出了一种新的AI方法，即用于水泥浆注浆性预测的进化最小二乘支持向量机推理模型（ELSIM-GP），以预测利用超细水泥浆注浆的注浆效果。在模型中，最小二乘支持向量机（LS-SVM）是一种受监督的机器学习技术，用于学习用于对高维数据进行分类的决策边界。差分演化（DE）集成到ELSIM-GP中，用于自动优化其调整参数。收集了240个砂质粉砂土注浆过程的历史案例，以训练，验证和测试推理模型。实验结果表明，ELSIM-GP在预测准确性方面可以克服其他基准方法。因此，所提出的方法是预测灌浆性的有前途的替代方法。

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